Optimised adaptive procedures and analysis methods for conducting speech-in-noise tests

Speech-in-noise testing is a valuable part of audiological test batteries. Test standardisation using precise methods is desirable for ease of administration. This study investigated the accuracy and reliability of different Bayesian and non-Bayesian adaptive procedures and analysis methods for conducting speech-in-noise testing.

Matrix sentence tests using different numbers of sentences (10, 20, 30 and 50) and target intelligibilities (50 and 75%) were simulated for modelled listeners with various characteristics. The accuracy and reliability of seven different measurement procedures and three different data analysis methods were assessed.

The estimation of 50% intelligibility was accurate and showed excellent reliability across the majority of methods tested, even with relatively few stimuli. Estimating 75% intelligibility resulted in decreased accuracy. For this target, more stimuli were required for sufficient accuracy and selected Bayesian procedures surpassed the performance of others. Some Bayesian procedures were also superior in the estimation of psychometric function width.

A single standardised procedure could improve the consistency of the matrix sentence test across a range of target intelligibilities. Candidate adaptive procedures and analysis methods are discussed. These could also be applicable for other speech materials. Further testing with human participants is required.